Marco Seeland

Luminescence imaging of polymer solar cells: Visualization of progressing degradation

We apply luminescence imaging as tool for the nondestructive visualization of degradation processes within bulk heterojunction polymer solar cells. The imaging technique is based on luminescence detection with a highly sensitive silicon charge-coupled-device camera and is able to visualize with time advancing degradation patterns of polymer solar cells. The devices investigated have been aged under defined conditions and were characterized periodically with current-voltage (I-V) sweeps. This allows determining the time evolution of the photovoltaic parameters and—in combination with the luminescence images—understanding differences in the observed degradation behavior. The versatile usability of the method is demonstrated in a correlation between local reduction of lateral luminescence and a fast decrease of the short-circuit current (Isc) due to the loss of active area. Differences in the degradation of photovoltaic parameters under varied aging conditions are discussed.

Quantitative analysis of electroluminescence images from polymer solar cells

We introduce the micro-diode-model (MDM) based on a discrete network of interconnected diodes, which allows for quantitative description of lateral electroluminescence emission images obtained from organic bulk heterojunction solar cells. Besides the distributed solar cell description, the equivalent circuit, respectively, network model considers interface and bulk resistances as well as the sheet resistance of the semitransparent electrode. The application of this model allows direct calculation of the lateral current and voltage distribution within the solar cell and thus accounts well for effects known as current crowding. In addition, network parameters such as internal resistances and the sheet-resistance of the higher resistive electrode can be determined. Furthermore, upon introduction of current sources the micro-diode-model also is able to describe and predict current-voltage characteristics for solar cell devices under illumination. The local nature of this description yields important conclusio...

Plant species classification using flower images—A comparative study of local feature representations

Steady improvements of image description methods induced a growing interest in image-based plant species classification, a task vital to the study of biodiversity and ecological sensitivity. Various techniques have been proposed for general object classification over the past years and several of them have already been studied for plant species classification. However, results of these studies are selective in the evaluated steps of a classification pipeline, in the utilized datasets for evaluation, and in the compared baseline methods. No study is available that evaluates the main competing methods for building an image representation on the same datasets allowing for generalized findings regarding flower-based plant species classification. The aim of this paper is to comparatively evaluate methods, method combinations, and their parameters towards classification accuracy. The investigated methods span from detection, extraction, fusion, pooling, to encoding of local features for quantifying shape and color information of flower images. We selected the flower image datasets Oxford Flower 17 and Oxford Flower 102 as well as our own Jena Flower 30 dataset for our experiments. Findings show large differences among the various studied techniques and that their wisely chosen orchestration allows for high accuracies in species classification. We further found that true local feature detectors in combination with advanced encoding methods yield higher classification results at lower computational costs compared to commonly used dense sampling and spatial pooling methods. Color was found to be an indispensable feature for high classification results, especially while preserving spatial correspondence to gray-level features. In result, our study provides a comprehensive overview of competing techniques and the implications of their main parameters for flower-based plant species classification.

Automated plant species identification—Trends and future directions

Current rates of species loss triggered numerous attempts to protect and conserve biodiversity. Species conservation, however, requires species identification skills, a competence obtained through intensive training and experience. Field researchers, land managers, educators, civil servants, and the interested public would greatly benefit from accessible, up-to-date tools automating the process of species identification. Currently, relevant technologies, such as digital cameras, mobile devices, and remote access to databases, are ubiquitously available, accompanied by significant advances in image processing and pattern recognition. The idea of automated species identification is approaching reality. We review the technical status quo on computer vision approaches for plant species identification, highlight the main research challenges to overcome in providing applicable tools, and conclude with a discussion of open and future research thrusts.

Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain

BackgroundAutomated species identification is a long term research subject. Contrary to flowers and fruits, leaves are available throughout most of the year. Offering margin and texture to characterize a species, they are the most studied organ for automated identification. Substantially matured machine learning techniques generate the need for more training data (aka leaf images). Researchers as well as enthusiasts miss guidance on how to acquire suitable training images in an efficient way.MethodsIn this paper, we systematically study nine image types and three preprocessing strategies. Image types vary in terms of in-situ image recording conditions: perspective, illumination, and background, while the preprocessing strategies compare non-preprocessed, cropped, and segmented images to each other. Per image type-preprocessing combination, we also quantify the manual effort required for their implementation. We extract image features using a convolutional neural network, classify species using the resulting feature vectors and discuss classification accuracy in relation to the required effort per combination.ResultsThe most effective, non-destructive way to record herbaceous leaves is to take an image of the leaf’s top side. We yield the highest classification accuracy using destructive back light images, i.e., holding the plucked leaf against the sky for image acquisition. Cropping the image to the leaf’s boundary substantially improves accuracy, while precise segmentation yields similar accuracy at a substantially higher effort. The permanent use or disuse of a flash light has negligible effects. Imaging the typically stronger textured backside of a leaf does not result in higher accuracy, but notably increases the acquisition cost.ConclusionsIn conclusion, the way in which leaf images are acquired and preprocessed does have a substantial effect on the accuracy of the classifier trained on them. For the first time, this study provides a systematic guideline allowing researchers to spend available acquisition resources wisely while yielding the optimal classification accuracy.

Quantitative evaluation of inhomogeneous device operation in thin film solar cells by luminescence imaging

We present a method for quantitative evaluation of electroluminescence images from thin film solar cells. The method called “quantitative electroluminescence imaging” (QuELI) is based on decoupling local equivalent circuit parameters and allows calculation of the local current-density as well as the local series resistance and saturation current-density. By application of this method to electroluminescence images obtained from polymer-fullerene based solar cells, we show that QuELI allows efficient separation between: (a) properties of the electrodes and their associated interfaces by the local series resistance and (b) properties of the active layer by the saturation current-density. We furthermore reveal large scale lateral phase separation via the strong variation in the saturation current-density, which delivers information on the energetic difference of thermal activation of charge carriers across the effective active band gap.

Efficiently Annotating Object Images with Absolute Size Information Using Mobile Devices

The projection of a real world scenery to a planar image sensor inherits the loss of information about the 3D structure as well as the absolute dimensions of the scene. For image analysis and object classification tasks, however, absolute size information can make results more accurate. Today, the creation of size annotated image datasets is effort intensive and typically requires measurement equipment not available to public image contributors. In this paper, we propose an effective annotation method that utilizes the camera within smart mobile devices to capture the missing size information along with the image. The approach builds on the fact that with a camera, calibrated to a specific object distance, lengths can be measured in the object’s plane. We use the camera’s minimum focus distance as calibration distance and propose an adaptive feature matching process for precise computation of the scale change between two images facilitating measurements on larger object distances. Eventually, the measured object is segmented and its size information is annotated for later analysis. A user study showed that humans are able to retrieve the calibration distance with a low variance. The proposed approach facilitates a measurement accuracy comparable to manual measurement with a ruler and outperforms state-of-the-art methods in terms of accuracy and repeatability. Consequently, the proposed method allows in-situ size annotation of objects in images without the need for additional equipment or an artificial reference object in the scene.

What Photons Tell Us about Solar Cells: Imaging Diagnostic Techniques

Photon emission by solar cells can be used for advanced device diagnostics with imaging techniques. We demonstrate that band-to-band luminescence, heat radiation, dislocation luminescence, and junction-breakdown-related radiation can be used to measure and detect local series resistances, hot-spots, defect-induced junction breakdown, and bulk-, surface- and grain-boundary recombination in silicon solar cells. First measurements of degradation and contamination of polymer solar cells are also presented.